Knowledge-augmented Methods for Natural Language Processing (SpringerBriefs in Computer Science)

by: Meng Jiang (Author),Bill Yuchen Lin(Author),Shuohang Wang(Author),Yichong Xu(Author),Wenhao Yu(Author),Chenguang Zhu(Author)

Publisher: Springer

Edition: 1st ed. 2024

Publication Date: 2024/4/11

Language: English

ISBN-10: 9819707463

ISBN-13: 9789819707461

Book Description

Over the last few years, natural language processing has seen remarkable progress due to the emergence of larger-scale models, better training techniques, and greater availability of data. Examples of these advancements include GPT-4, ChatGPT, and other pre-trained language models. These models are capable of characterizing linguistic pattes and generating context-aware representations, resulting in high-quality output. However, these models rely solely on input-output pairs during training and, therefore, struggle to incorporate exteal world knowledge, such as named entities, their relations, common sense, and domain-specific content. Incorporating knowledge into the training and inference of language models is critical to their ability to represent language accurately. Additionally, knowledge is essential in achieving higher levels of intelligence that cannot be attained through statistical leaing of input text pattes alone. In this book, we will review recent developments in the field of natural language processing, specifically focusing on the role of knowledge in language representation. We will examine how pre-trained language models like GPT-4 and ChatGPT are limited in their ability to capture exteal world knowledge and explore various approaches to incorporate knowledge into language models. Additionally, we will discuss the significance of knowledge in enabling higher levels of intelligence that go beyond statistical leaing on input text pattes. Overall, this survey aims to provide insights into the importance of knowledge in natural language processing and highlight recent advances in this field.

About the Author

Over the last few years, natural language processing has seen remarkable progress due to the emergence of larger-scale models, better training techniques, and greater availability of data. Examples of these advancements include GPT-4, ChatGPT, and other pre-trained language models. These models are capable of characterizing linguistic pattes and generating context-aware representations, resulting in high-quality output. However, these models rely solely on input-output pairs during training and, therefore, struggle to incorporate exteal world knowledge, such as named entities, their relations, common sense, and domain-specific content. Incorporating knowledge into the training and inference of language models is critical to their ability to represent language accurately. Additionally, knowledge is essential in achieving higher levels of intelligence that cannot be attained through statistical leaing of input text pattes alone. In this book, we will review recent developments in the field of natural language processing, specifically focusing on the role of knowledge in language representation. We will examine how pre-trained language models like GPT-4 and ChatGPT are limited in their ability to capture exteal world knowledge and explore various approaches to incorporate knowledge into language models. Additionally, we will discuss the significance of knowledge in enabling higher levels of intelligence that go beyond statistical leaing on input text pattes. Overall, this survey aims to provide insights into the importance of knowledge in natural language processing and highlight recent advances in this field.